Wind shear coefficients (WSCs) are fundamental parameters for wind resource assessment, wind turbine design, and offshore wind farm operation. This work focuses on positive wind shear coefficients (WSCs); hereafter, WSC refers exclusively to positive-value wind shear coefficients derived from positive shear profiles. However, the characteristics of positive WSCs in the offshore waters of Zhuanghe have not yet been systematically investigated. In this study, two years of in situ observational data (2015–2017) were analyzed to characterize the temporal variability of positive WSCs under different atmospheric stability conditions. The results indicate that the atmospheric boundary layer in the study area exhibits pronounced seasonal and diurnal variations in stability, with stable and unstable conditions showing a distinct annual cycle. Increasing wind speed promotes neutral conditions as mechanically generated turbulence becomes dominant over buoyancy effects. The WSC ranges from 0.01 to 0.32 and exhibits pronounced seasonal and monthly variability. Under neutral and unstable atmospheric conditions, the diurnal variation in WSC is relatively consistent among different measurement heights, whereas stable conditions produce appreciable vertical differences in WSC throughout most of the year. These findings improve our understanding of offshore wind shear characteristics across various atmospheric stability regimes. Furthermore, they offer valuable guidance for offshore wind resource assessment, wind profile modeling, and the overall design and operation of wind farms in the Zhuanghe region.

Wind shear coefficients (WSCs) are fundamental parameters for wind resource assessment, wind turbine design, and offshore wind farm operation. However, the characteristics of WSCs in the offshore waters of Zhuanghe have not yet been systematically investigated. In this study, two years of in-situ observational data (2015-2017) were analyzed to characterize the temporal variability of WSCs under different atmospheric stability conditions. The results indicate that the atmospheric boundary layer in the study area is dominated by stable and unstable conditions, exhibiting a distinct U-shaped annual distribution. The diurnal evolution of atmospheric stability varies considerably among months and is strongly associated with both wind speed and wind direction. Under low wind speed conditions, stable and unstable atmospheres occur with comparable frequencies. As wind speed increases, the occurrence of neutral conditions increases steadily, and the atmosphere becomes predominantly neutral when wind speed exceeds 18.5 m s^-1. Northerly winds are generally associated with unstable atmospheric conditions, whereas southerly winds are primarily associated with stable conditions. The WSC ranges from 0.01 to 0.32 and exhibits pronounced seasonal and monthly variability. Under neutral and unstable atmospheric conditions, the diurnal variation of WSC is relatively consistent across different measurement heights. In contrast, under stable conditions, significant vertical differences in WSC are observed throughout most of the year, except in March and April. These findings improve the understanding of offshore wind shear characteristics under different atmospheric stability regimes and provide valuable guidance for offshore wind resource assessment, wind profile modeling, and the design, operation, and optimization of offshore wind farms in the Zhuanghe offshore region.

Accurate and timely wildfire hotspot detection is essential for ecological sustainability and supporting climate resilience strategies. Although sensors such as MODIS and VIIRS have been widely used for wildfire detection, the potential of ocean color satellites for terrestrial wildfire monitoring remains largely unexplored. In this study, a Spectral–Spatial Attention U-Net (SSA-UNet) framework is proposed for wildfire hotspot detection using multispectral observations from the HY-1E Coastal Zone Color Scanner II (COCTS2) over the Three-North region of China. The proposed framework integrates spectral attention to enhance fire-sensitive bands and spatial attention to capture contextual wildfire patterns under complex environmental conditions. Experimental results show that SSA-UNet achieves a Precision of 0.8913, Recall of 0.7961, and F1-score of 0.8680, outperforming conventional threshold-based approaches and baseline deep learning models. Ablation experiments further demonstrate the effectiveness of the spectral–spatial attention mechanism, while band analysis highlights the important contributions of near-infrared, shortwave infrared, and thermal infrared observations for wildfire hotspot detection. The real wildfire case analysis further confirms the practical applicability of the proposed framework. The results demonstrate that HY-1E COCTS2 data have considerable potential for large-scale terrestrial wildfire monitoring when combined with deep learning techniques.